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Agent skill

Prompt Engineering Patterns

by lamm-mit in lamm-mit/scienceclaw

Generate optimized LLM prompts using chain-of-thought, ReAct, and other scientific reasoning patterns

Apache-2.0Auto-check passedAI & LLM Engineering

Install Prompt Engineering Patterns

skills CLI
$ npx skills add lamm-mit/scienceclaw --skill prompt-engineering-patterns -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install lamm-mit/scienceclaw prompt-engineering-patterns --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prompt-engineering-patterns .claude/skills/prompt-engineering-patterns && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
prompt-engineering-patterns
GitHub stars
244
Token cost
~522 tokens
SKILL.md length
54 words
Files
3 (incl. scripts)
Skills in repo
85
Repo updated
First seen
Licence
Apache-2.0

At a glance

Generate optimized LLM prompts using chain-of-thought, ReAct, and other scientific reasoning patterns

  • Tasks that involve Prompt engineering
  • SKILL.md covers Overview, Usage and Output Format
  • Runs Python scripts from its folder; calls python3

What it does

Prompt Engineering Patterns is an agent skill from lamm-mit/scienceclaw. Generate optimized LLM prompts using chain-of-thought, ReAct, and other scientific reasoning patterns

Its SKILL.md is about 520 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/prompt_optimize.py`).

It sits in AI & LLM Engineering, covering Prompt engineering. It works with React. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Prompt engineering

Example prompts

  • “/prompt-engineering-patterns”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit ab9aba1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prompt Engineering Patterns loads about 522 tokens when it runs. Until then it costs about 32 tokens; SKILL.md has 54 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~32
When it runs · the whole SKILL.md, loaded when a task matches
~522

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 54 words, ~522 tokens.

Download SKILL.mdSave it as .claude/skills/prompt-engineering-patterns/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
prompt-engineering-patterns
description
Generate optimized LLM prompts using chain-of-thought, ReAct, and other scientific reasoning patterns

Overview

Advanced LLM prompt optimization patterns for scientific reasoning: chain-of-thought, tree-of-thought, few-shot learning, ReAct, and self-consistency. Generates optimized prompts tailored to scientific investigation tasks and specific domains (biology, chemistry, materials, etc.).

Use this tool to construct better prompts before querying an LLM, ensuring rigorous scientific reasoning, hypothesis generation, and evidence-based conclusions.

Usage

bash
# Generate a chain-of-thought prompt for a biology task
python3 skills/prompt-engineering-patterns/scripts/prompt_optimize.py \
  --task "Identify potential drug targets for Alzheimer's disease" \
  --pattern chain-of-thought \
  --domain biology

# Generate a ReAct prompt for tool-using agents
python3 skills/prompt-engineering-patterns/scripts/prompt_optimize.py \
  --task "Predict BBB permeability of novel kinase inhibitors" \
  --pattern react \
  --domain chemistry

# Generate a tree-of-thought prompt
python3 skills/prompt-engineering-patterns/scripts/prompt_optimize.py \
  --task "Evaluate CRISPR delivery mechanisms" \
  --pattern tree-of-thought

# Generate few-shot prompt for a specific scientific task
python3 skills/prompt-engineering-patterns/scripts/prompt_optimize.py \
  --task "Classify protein-protein interactions from sequence features" \
  --pattern few-shot \
  --domain biology

Output Format

json
{
  "pattern": "chain-of-thought",
  "task": "Identify potential drug targets for Alzheimer's disease",
  "optimized_prompt": "You are an expert computational biologist...\n\nTask: Identify potential drug targets for Alzheimer's disease\n\nLet's think through this step by step:\n1. First, consider the molecular mechanisms...",
  "explanation": "Chain-of-thought prompting elicits step-by-step reasoning, improving accuracy on complex scientific tasks by up to 40% compared to direct answering."
}

© lamm-mit, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in skills/prompt-engineering-patterns of lamm-mit/scienceclaw.

  • SKILL.md
  • scripts/__pycache__/prompt_optimize.cpython-313.pyc
  • scripts/prompt_optimize.py

Open the folder on GitHubat commit ab9aba1

Compare with similar skills

Prompt Engineering Patterns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Prompt Engineering Patterns compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Prompt Engineering Patterns this skilllamm-mit/scienceclaw244—~522Automated safety check: PassApache-2.0
Canvas TemplatesPostHog/code179—~2.2kAutomated safety check: PassMIT
Building Agent Systemstelagod/code-abyss244—~691Automated safety check: PassMIT
Prompt Engineermajiayu000/claude-skill-registry6661 repos~794Automated safety check: PassMIT
Dspymagnus919/agent-skills111—~2kAutomated safety check: PassMIT
Prompt Improverseverity1/claude-code-prompt-improver1.9k2 repos~1.7kAutomated safety check: PassMIT

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Works with

Categories

Questions about Prompt Engineering Patterns

What does Prompt Engineering Patterns do?

Generate optimized LLM prompts using chain-of-thought, ReAct, and other scientific reasoning patterns. Prompt Engineering Patterns is an agent skill from lamm-mit/scienceclaw.

When should I use Prompt Engineering Patterns?

Prompt Engineering Patterns fits situations like: tasks that involve Prompt engineering.

How do I install Prompt Engineering Patterns in Claude Code?

Run `npx skills add lamm-mit/scienceclaw --skill prompt-engineering-patterns -a claude-code`. Or copy the skill folder (skills/prompt-engineering-patterns in lamm-mit/scienceclaw) into .claude/skills/prompt-engineering-patterns in your project. Claude Code loads it when a task matches its description.

How do I install Prompt Engineering Patterns in Codex?

Run `npx skills add lamm-mit/scienceclaw --skill prompt-engineering-patterns -a codex`. Or copy the skill folder (skills/prompt-engineering-patterns in lamm-mit/scienceclaw) into .agents/skills/prompt-engineering-patterns in your project. Codex loads it when a task matches its description.

Can I use Prompt Engineering Patterns in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lamm-mit/scienceclaw --skill prompt-engineering-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-engineering-patterns, .gemini/skills/prompt-engineering-patterns, .github/skills/prompt-engineering-patterns and .opencode/skills/prompt-engineering-patterns in your project.

What does Prompt Engineering Patterns need to run?

Going by SKILL.md and its folder, Prompt Engineering Patterns needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Prompt Engineering Patterns access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Prompt Engineering Patterns safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Prompt Engineering Patterns use?

Prompt Engineering Patterns is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prompt Engineering Patterns use?

About 522 tokens (SKILL.md is roughly 2.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Prompt Engineering Patterns?

Skills that share tags, products or a category with Prompt Engineering Patterns: Canvas Templates (PostHog/code, 179 stars), Building Agent Systems (telagod/code-abyss, 244 stars), Prompt Engineer (majiayu000/claude-skill-registry, 666 stars) and Dspy (magnus919/agent-skills, 111 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prompt Engineering Patterns?

lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 21, 2026.

Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.